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• FORECASTING STUDENT MOVEMENT USING MACHINE LEARNING AND TIME SERIES ANALYSIS

  Mirziyod Adkham ugli Radjapov , К.D. Chemukhin , L. E. Petrosyan

  134-151

  2026-07-07

  Abstract ▼

  Managing student mobility amid demographic fluctuations and the digitalization of higher education is becoming a key factor in university sustainability, affecting both financial performance and the quality of the educational process. The increasing complexity of processes such as admissions, withdrawals, academic leaves of absence, transfers, and reinstatements requires a shift from expert assessments to formalized models and predictive analytics based on the processing of large datasets. The aim of this study is to develop and evaluate the effectiveness of a model for forecasting student population dynamics based on machine learning algorithms and using time series analysis. Aggregated statistical data on student mobility at Russian and Chinese universities for the period 2013–2024 were used as the empirical basis, which allowed for consideration of both the structural features of national higher education systems and long-term trends and anomalous events (including the impact of the COVID-19 pandemic). The methodological framework includes a dynamic student cohort balance model in the form of a system of recurrent equations describing transitions between academic years and enrollment statuses, and an additive Prophet model used for independent forecasting of key flows (admissions, transfers, withdrawals, academic leaves of absence, reinstatements) as separate time series. The software implementation is based on the FastAPI–React stack, utilizing the SQLAlchemy ORM layer and mechanisms for caching the results of predictive calculations, which ensures high performance when processing queries. Experimental results on real data demonstrate the robustness of the developed model to nonlinear changes in the input series and confirm the feasibility of integrating machine learning into the student movement management system. The practical significance of this work lies in the creation of an information and analytical system that provides automated monitoring and forecasting of student movement trajectories between courses and statuses, enabling universities to transition from reactive to proactive planning of admissions campaigns, classroom allocation, and the distribution of personnel and infrastructure resources.